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Context: Individualized fracture risk may help to select patients requiring a pharmacological treatment for osteoporosis. FRAX and the Garvan fracture risk calculators are the most used tools, although their external validation has shown significant differences in their risk prediction ability., Objective and Methods: Using data from the Fracture Risk Brussels Epidemiological Enquiry study, a cohort of 3560 postmenopausal women aged 60 to 85 years, we aimed to construct original 5-year fracture risk prediction models using validated clinical risk factors (CRFs). Three models of competing risk analysis were developed to predict major osteoporotic fractures (MOFs), all fractures, and central fractures (femoral neck, shoulder, clinical spine, pelvis, ribs, scapula, clavicle, sternum)., Results: Age, a history of fracture, and hip or spine BMD were predictors common to the 3 models. Excessive alcohol intake and the presence of comorbidities were specific additional CRFs for MOFs, a history of fall for all fractures, and rheumatoid arthritis for central fractures. Our models predicted the fracture probability at 5 years with an acceptable accuracy (Brier scores ≤ 0.1) and had a good discrimination power (area under the receiver operating curve of 0.73 for MOFs and 0.72 for central fractures) when internally validated by bootstrap. Three simple nomograms, integrating significant CRFs and the mortality risk, were constructed for different fracture sites. In conclusion, we derived 3 models predicting fractures with an acceptable accuracy, particularly for MOFs and central fractures. The models are based on a limited number of CRFs, and we constructed nomograms for use in clinical practice., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.)

Patients who sustain a fracture are at greatest risk of recurrent fracture during the next 2 years. We propose three models to identify subjects most at risk of an imminent fracture, according to fracture site (any fracture, major osteoporotic fracture [MOF] or central). They were constructed using data of the prospective Frisbee cohort, which includes 3560 postmenopausal women aged 60 to 85 years who were followed for at least 5 years. A total of 881 subjects had a first incident validated fragility fracture before December 2018. Among these, we validated 130 imminent fractures occurring within the next 2 years; 79 were MOFs, and 88 were central fractures. Clinical risk factors were re-evaluated at the time of the index fracture. Fine and Gray proportional hazard models were derived separately for each group of fractures. The following risk factors were significantly associated with the risk of any imminent fracture: total hip bone mineral density (BMD) (p < 0.001), a fall history (p < 0.001), and comorbidities (p = 0.03). Age (p = 0.05 and p = 0.03, respectively) and a central fracture as the index fracture (p = 0.04 and p = 0.005, respectively) were additional predictors of MOFs and central fractures. The three prediction models are presented as nomograms. The calibration curves and the Brier scores based on bootstrap resampling showed calibration scores of 0.089 for MOF, 0.094 for central fractures, and 0.132 for any fractures. The predictive accuracy of the models expressed as area under the receiver operating characteristic (AUROC) curve (AUC) were 0.74 for central fractures, 0.72 for MOFs, and 0.66 for all fractures, respectively. These AUCs compare well with those of FRAX and Garvan to predict the 5- or 10-year fracture probability. In summary, five predictors (BMD, age, comorbidities, falls, and central fracture as the incident fracture) allow the calculation with a reasonable accuracy of the imminent risk of fracture at different sites (MOF, central fracture, and any fracture) after a recent sentinel fracture. © 2021 American Society for Bone and Mineral Research (ASBMR)., (© 2021 American Society for Bone and Mineral Research (ASBMR).)

Introduction: Hospitalization is associated with acute changes in sarcopenia status in older people, but the influencing factors are not fully understood. Pre-admission care dependency level as a risk factor has not yet been investigated., Objective: Evaluate if pre-admission care dependency level is an independent predictor of sarcopenia changes following hospitalization., Setting and Subjects: Data came from the Sarcopenia 9+ EAMA Project, a European prospective multi-centre study. For this study, 227 hospitalised older people were included from four different hospitals in Belgium, Spain and Poland, between 18 February 2019 and 5 September 2020., Methods: Sarcopenia status at admission and discharge were calculated using a combined score (desirability value) based on muscle mass (calf circumference), strength (grip) and function (walking speed). Ratio of admission to discharge status was the outcome (desirability ratio; 1.00 meaning no difference). Predictor variable was the pre-admission care dependency level, classified into three groups: independent older people living at home, dependent older people living at home and older people living in a care home. Linear regression models were applied, considering potential confounders., Results: Mean desirability ratio for dependent older people living at home ('middle dependent group') was lower (0.89) compared to independent older people (0.98; regression coefficient -0.09 [95% CI -0.16, -0.02]) and care home patients (1.05; -0.16 [95% CI -0.01, -0.31]). Adjusting for potential confounders or using another statistical approach did not affect the main results., Conclusion: Dependent older people living at home were at higher risk of deterioration in sarcopenia status following hospitalization. In-depth studies investigating causes and potential interventions of these findings are needed., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Probabilistic models including clinical risk factors with or without bone mineral density (BMD) have been developed to estimate the 5- or 10-year absolute fracture risk. We investigated the performance of the FRAX and Garvan tools in a well-characterized population-based cohort of 3560 postmenopausal, volunteer women, aged 60 to 85 years at baseline, included in the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) cohort, during 5 years of follow-up. Baseline data were used to calculate the estimated 10-year risk of hip and major osteoporotic fractures (MOFs) for each participant using FRAX (Belgium). We computed the 5-year risk according to the Garvan model with BMD. For calibration, the predicted risk of fracture was compared with fracture incidence across a large range of estimated fracture risks. The accuracy of the calculators to predict fractures was assessed using the area under the receiver operating characteristic curves (AUC). The FRAX tool was well calibrated for hip fractures (slope 1.09, p  < 0.001; intercept -0.001, p  = 0.46), but it consistently underestimated the incidence of major osteoporotic fractures (MOFs) (slope 2.12, p  < 0.001; intercept -0.02, p  = 0.06). The Garvan tool was well calibrated for "any Garvan" fractures (slope 1.05, p  < 0.001; intercept 0.01, p  = 0.37) but largely overestimated the observed hip fracture rate (slope 0.32, p  < 0.001; intercept 0.006, p  = 0.05). The predictive value for hip fractures was better for FRAX (AUC: 0.841, 95% confidence interval [CI] 0.795-0.887) than for Garvan (AUC: 0.769, 95% CI 0.702-0.836, p  = 0.01). The Garvan AUC for "any Garvan" fractures was 0.721 (95% CI 0.693-0.749) and FRAX AUC for MOFs was 0.708 (95% CI 0.675-0.741). In conclusion, in our Belgian cohort, FRAX estimated quite well hip fractures but underestimated MOFs, while Garvan overestimated hip fracture risk but showed a good estimation of "any Garvan" fractures. Both models had a good discriminatory value for hip fractures but only a moderate discriminatory ability for MOFs or "any Garvan" fractures. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research., (© 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.)